Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

INTRODUCTION: Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. METHODS: We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). RESULTS: Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. DISCUSSION: Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. HIGHLIGHTS: Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens.

OBJECTIVES: Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. METHODS: We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. RESULTS: In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. CONCLUSION: The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.

Independent effects of amyloid and vascular markers on long-term functional outcomes: An 8-year longitudinal study of subcortical vascular cognitive impairment.

BACKGROUND AND PURPOSE: Subcortical vascular cognitive impairment (SVCI) is characterized by the presence of cerebral small vessel disease (CSVD) markers. Some SVCI patients also show Alzheimer's disease and cerebral amyloid angiopathy markers. However, the effects of these imaging markers on long-term clinical outcomes have not yet been established. The present study, therefore, aimed to determine how these imaging markers influence functional disability and/or mortality. METHODS: We recruited 194 participants with SVCI from the memory clinic and followed them up. All participants underwent brain magnetic resonance imaging at baseline, and 177 (91.2%) participants underwent beta-amyloid (Aß) positron emission tomography. We examined the occurrence of ischemic or hemorrhagic strokes. We also evaluated functional disability and mortality using the modified Rankin scale. To determine the effects of imaging markers on functional disability or mortality, we used Fine and Gray competing regression or Cox regression analysis. RESULTS: During a 8.6-year follow-up period, 46 of 194 patients (23.7%) experienced a stroke, 110 patients (56.7%) developed functional disabilities and 75 (38.6%) died. Aß positivity (subdistribution hazard ratio [SHR] = 2.73), greater white matter hyperintensity (WMH) volume (SHR = 3.11) and ≥3 microbleeds (SHR = 2.29) at baseline were independent predictors of functional disability regardless of the occurrence of stroke. Greater WMH volume (hazard ratio = 2.07) was an independent predictor of mortality. CONCLUSIONS: Our findings suggest that diverse imaging markers may predict long-term functional disability and mortality in patients with SVCI, which in turn may provide clinicians with a more insightful understanding of the long-term outcomes of SVCI.

Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier.

PURPOSE: In this study, we used machine learning to develop a new method derived from a ligand-independent amyloid (Aß) positron emission tomography (PET) classifier to harmonise different Aß ligands. METHODS: We obtained 107 paired 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) PET images at the Samsung Medical Centre. To apply the method to FMM ligand, we transferred the previously developed FBB PET classifier to test similar features from the FMM PET images for application to FMM, which in turn developed a ligand-independent Aß PET classifier. We explored the concordance rates of our classifier in detecting cortical and striatal Aß positivity. We investigated the correlation of machine learning-based cortical tracer uptake (ML-CTU) values quantified by the classifier between FBB and FMM. RESULTS: This classifier achieved high classification accuracy (area under the curve = 0.958) even with different Aß PET ligands. In addition, the concordance rate of FBB and FMM using the classifier (87.5%) was good to excellent, which seemed to be higher than that in visual assessment (82.7%) and lower than that in standardised uptake value ratio cut-off categorisation (93.3%). FBB and FMM ML-CTU values were highly correlated with each other (R = 0.903). CONCLUSION: Our findings suggested that our novel classifier may harmonise FBB and FMM ligands in the clinical setting which in turn facilitate the biomarker-guided diagnosis and trials of anti-Aß treatment in the research field.

Strictly Lobar Microbleeds Reflect Amyloid Angiopathy Regardless of Cerebral and Cerebellar Compartments.

BACKGROUND AND PURPOSE: We aimed to determine whether lobar cerebellar microbleeds or concomitant lobar cerebellar and deep microbleeds, in the presence of lobar cerebral microbleeds, attribute to underlying advanced cerebral amyloid angiopathy pathology or hypertensive arteriopathy. METHODS: We categorized 71 patients with suspected cerebral amyloid angiopathy markers (regardless of the presence of deep and cerebellar microbleeds) into 4 groups according to microbleed distribution: L (strictly lobar cerebral, n=33), L/LCbll (strictly lobar cerebral and strictly lobar cerebellar microbleeds, n=13), L/Cbll/D (lobar, cerebellar, and deep microbleeds, n=17), and L/D (lobar and deep, n=8). We additionally categorized patients with cerebellar microbleeds into 2 groups according to dentate nucleus involvement: strictly lobar cerebellar (n=16) and dentate (n=14). We then compared clinical characteristics, Aß (amyloid-ß) positivity on PET (positron emission tomography), magnetic resonance imaging cerebral amyloid angiopathy markers, and cerebral small vessel disease burden among groups. RESULTS: The frequency of Aß positivity was higher in the L and L/LCbll groups (81.8% and 84.6%) than in the L/Cbll/D and L/D groups (37.5% and 29.4%; P<0.001), while lacune numbers were lower in the L and L/LCbll groups (1.7±3.3 and 1.7±2.6) than in the L/Cbll/D and L/D groups (8.0±10.3 and 13.4±17.7, P=0.001). The L/LCbll group had more lobar cerebral microbleeds than the L group (93.2±121.8 versus 38.0±40.8, P=0.047). The lobar cerebellar group had a higher Aß positivity (75% versus 28.6%, P=0.011) and lower lacune number (2.3±3.7 versus 8.6±1.2, P=0.041) than the dentate group. CONCLUSIONS: Strictly lobar cerebral and cerebellar microbleeds are related to cerebral amyloid angiopathy, whereas any combination of concurrent lobar and deep microbleeds suggest hypertensive angiopathy regardless of cerebral or cerebellar compartments.

Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

Incorrect acknowledgments.

Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

PURPOSE: We developed a machine learning-based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes. METHODS: A total of 337 18F-florbetaben PET scans obtained at Samsung Medical Center were assessed. We defined a feature vector representing the change in PET tracer uptake from grey to white matter. Using support vector machine (SVM) regression and SVM classification, we quantified the cortical uptake as predicted regional cortical tracer uptake (pRCTU) and categorised the scans as positive and negative. Positive scans were further classified into two stages according to the striatal uptake. We compared outcome parameters among stages and further assessed the association between the pRCTU and outcome variables. Finally, we performed path analysis to determine mediation effects between PET variables. RESULTS: The classification accuracy was 97.3% for cortical amyloid positivity and 91.1% for striatal positivity. The left frontal and precuneus/posterior cingulate regions, as well as the anterior portion of the striatum, were important in determination of stages. The clinical scores and magnetic resonance imaging parameters showed negative associations with PET stage. However, except for the hippocampal volume, most outcomes were associated with the stage through the complete mediation effect of pRCTU. CONCLUSION: Using a machine learning algorithm, we achieved high accuracy for in vivo amyloid PET staging. The in vivo amyloid stage was associated with cognitive function and cerebral atrophy mostly through the mediation effect of cortical amyloid.

Correction to: Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

The Table 2 in the original version of this article contained a mistake in the alignment. Correct Table 2 presentation is presented here.

Correction to: A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB.

Funding information from the original version of this article was incomplete. Complete information is presented here.

A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB.

PURPOSE: We developed a new method to directly calculate Centiloid (CL) units of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) without conversion to the PiB standardized uptake value ratio (SUVR). METHODS: Paired FBB and FMM PET scans were obtained from 20 Alzheimer's disease-related cognitive impairment patients, 16 old controls, and 20 young controls. We investigated the correlations between the FBB and FMM CL units using the direct comparison of FBB-FMM CL (dcCL) method and the standard CL method and compare differences in FBB and FMM CL units between dcCL method and the standard method. RESULTS: Following the conversion of FBB or FMM SUVRs into CL units, a direct relationship was formed between the FBB or FMM SUVRs and the CL units using dcCL method (FBB dcCL = 151.42 × FBB dcSUVR - 142.24 and FMM dcCL = 148.52 × FMM dcSUVR - 137.09). The FBB and FMM CL units were highly correlated in both our method (R2 = 0.97, FMM dcCL = 0.97 × FBB dcCL + 1.64) and the standard method (R2 = 0.97, FMM CLstandard = 0.79 × FBB CLstandard + 1.36). However, the CL variations between FBB and FMM were smaller when calculated by dcCL method (6.15) than when calculated by the previous method (10.22; P = 0.01). CONCLUSIONS: Our findings suggest that our direct comparison of FBB-FMM method, rather than the standard method, is a reasonable way to convert FBB or FMM SUVRs into CL units, at least in environments where FBB or FMM ligands are used frequently.

Application of an amyloid and tau classification system in subcortical vascular cognitive impairment patients.

OBJECTIVE: To apply an AT (Aß/tau) classification system to subcortical vascular cognitive impairment (SVCI) patients following recently developed biomarker-based criteria of Alzheimer's disease (AD), and to investigate its clinical significance. METHODS: We recruited 60 SVCI patients who underwent the neuropsychological tests, brain MRI, and 18F-florbetaben and 18F-AV1451 PET at baseline. As a control group, we further recruited 27 patients with AD cognitive impairment (ADCI; eight Aß PET-positive AD dementia and 19 amnestic mild cognitive impairment). ADCI and SVCI patients were classified as having normal or abnormal Aß (A-/A+) and tau (T-/T+) based on PET results. Across the three SVCI groups (A-, A+T-, and A+T+SVCI), we compared longitudinal changes in cognition, hippocampal volume (HV), and cortical thickness using linear mixed models. RESULTS: Among SVCI patients, 33 (55%), 20 (33.3%), and seven (11.7%) patients were A-, A+T-, and A+T+, respectively. The frequency of T+ was lower in A+SVCI (7/27, 25.9%) than in A+ADCI (14/20, 70.0%, p = 0.003) which suggested that cerebral small vessel disease affected cognitive impairments independently of A+. A+T-SVCI had steeper cognitive decline than A-SVCI. A+T+SVCI also showed steeper cognitive decline than A+T-SVCI. Also, A+T-SVCI had steeper decrease in HV than A-SVCI, while cortical thinning did not differ between the two groups. A+T+SVCI had greater global cortical thinning compared with A+T-SVCI, while declines in HV did not differ between the two groups. CONCLUSION: This study showed that the AT system successfully characterized SVCI patients, suggesting that the AT system may be usefully applied in a research framework for clinically diagnosed SVCI.

Cerebrospinal Fluid Biomarkers for the Diagnosis and Classification of Alzheimer's Disease Spectrum.

BACKGROUND: Cerebrospinal fluid (CSF) biomarkers are increasingly used in clinical practice for the diagnosis of Alzheimer's disease (AD). We aimed to 1) determine cutoff values of CSF biomarkers for AD, 2) investigate their clinical utility by estimating a concordance with amyloid positron emission tomography (PET), and 3) apply ATN (amyloid/tau/neurodegeneration) classification based on CSF results. METHODS: We performed CSF analysis in 51 normal controls (NC), 23 mild cognitive impairment (MCI) and 65 AD dementia (ADD) patients at the Samsung Medical Center in Korea. We attempted to develop cutoff of CSF biomarkers for differentiating ADD from NC using receiver operating characteristic analysis. We also investigated a concordance between CSF and amyloid PET results and applied ATN classification scheme based on CSF biomarker abnormalities to characterize our participants. RESULTS: CSF Aß42, total tau (t-tau) and phosphorylated tau (p-tau) significantly differed across the three groups. The area under curve for the differentiation between NC and ADD was highest in t-tau/Aß42 (0.994) followed by p-tau/Aß42 (0.963), Aß42 (0.960), t-tau (0.918), and p-tau (0.684). The concordance rate between CSF Aß42 and amyloid PET results was 92%. Finally, ATN classification based on CSF biomarker abnormalities led to a majority of NC categorized into A-T-N-(73%), MCI as A+T-N-(30%)/A+T+N+(26%), and ADD as A+T+N+(57%). CONCLUSION: CSF biomarkers had high sensitivity and specificity in differentiating ADD from NC and were as accurate as amyloid PET. The ATN subtypes based on CSF biomarkers may further serve to predict the prognosis.

A nanofilter composed of carbon nanotube-silver composites for virus removal and antibacterial activity improvement.

We have developed a new nanofilter using a carbon nanotube-silver composite material that is capable of efficiently removing waterborne viruses and bacteria. The nanofilter was subjected to plasma surface treatment to enhance its flow rate, which was improved by approximately 62%. Nanoscale pores were obtained by fabricating a carbon nanotube network and using nanoparticle fixation technology for the removal of viruses. The pore size of the nanofilter was approximately 38 nm and the measured flow rate ranged from 21.0 to 97.2L/(min·m(2)) under a pressure of 1-6 kgf/cm(2) when the amount of loaded carbon nanotube-silver composite was 1.0 mg/cm(2). The nanofilter was tested against Polio-, Noro-, and Coxsackie viruses using a sensitive real-time polymerase chain reaction assay to detect the presence of viral particles within the outflow. No trace of viruses was found to flow through the nanofilter with carbon nanotube-silver composite loaded above 0.8 mg/cm(2). Moreover, the surface of the filter has antibacterial properties to prevent bacterial clogging due to the presence of 20-nm silver nanoparticles, which were synthesized on the carbon nanotube surface.

Diffusion-Perfusion Mismatch in Single Subcortical Infarction: A Predictor of Early Neurological Deterioration and Poor Functional Outcome.

BACKGROUND/AIMS: Early neurological deterioration (END) is frequently observed and related to poor functional outcome in patients with single subcortical infarction (SSI). We evaluated the role of diffusion-perfusion mismatch (DPM) as a predictor of END and functional outcome in patients with SSI. METHODS: We retrospectively analyzed data for 274 patients with acute SSI. DPM was positive in the presence of a lesion on the perfusion map that was larger than that on the corresponding slice on diffusion-weighted imaging. END was defined as an increase of <1 points in the motor portion of the National Institute of Health Stroke Scale (NIHSS) within 72 h from MRI acquisition. Patients with a modified Rankin Scale (mRS) score of 3 or more at day 30 were considered having a poor functional outcome. RESULTS: DPM was more frequently observed in the END (+) than in the END (-) group (21/35 (60.0%) vs. 50/239 (20.9%); p < 0.001). After adjusting for covariates, the presence of DPM and NIHSS score on admission were independently associated with END (DPM, OR 5.03, p < 0.001; NIHSS, OR 1.14, p = 0.033) and poor functional outcome (DPM, OR 2.44, p = 0.018; NIHSS, OR 1.48, p < 0.001). CONCLUSIONS: The DPM concept is applicable to prediction of END and functional disability in patients with SSI.

Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment.

Objectives: Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI. Methods: We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-ß (Aß) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network. Results: The proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aß (+) than with Aß (-). Conclusion: The proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.

Distinct spatiotemporal patterns of cortical thinning in Alzheimer's disease-type cognitive impairment and subcortical vascular cognitive impairment.

Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.

Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET.

PURPOSE: The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional ß-amyloid (Aß) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aß pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment. PATIENTS AND METHODS: We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions. RESULTS: The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aß involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aß deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups. CONCLUSIONS: The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.

Distinct effects of blood pressure parameters on Alzheimer's and vascular markers in 1,952 Asian individuals without dementia.

BACKGROUND: Risk factors for cardiovascular disease, including elevated blood pressure, are known to increase risk of Alzheimer's disease. There has been increasing awareness of the relationship between long-term blood pressure (BP) patterns and their effects on the brain. We aimed to investigate the association of repeated BP measurements with Alzheimer's and vascular disease markers. METHODS: We recruited 1,952 participants without dementia between August 2015 and February 2022. During serial clinic visits, we assessed both systolic BP (SBP) and diastolic BP (DBP), and visit-to-visit BP variability (BPV) was quantified from repeated measurements. In order to investigate the relationship of mean SBP (or DBP) with Alzheimer's and vascular markers and cognition, we performed multiple linear and logistic regression analyses after controlling for potential confounders (Model 1). Next, we investigated the relationship of with variation of SBP (or DBP) with the aforementioned variables by adding it into Model 1 (Model 2). In addition, mediation analyses were conducted to determine mediation effects of Alzheimer's and vascular makers on the relationship between BP parameters and cognitive impairment. RESULTS: High Aß uptake was associated with greater mean SBP (ß = 1.049, 95% confidence interval 1.016-1.083). High vascular burden was positively associated with mean SBP (odds ratio = 1.293, 95% CI 1.015-1.647) and mean DBP (1.390, 1.098-1.757). High tau uptake was related to greater systolic BPV (0.094, 0.001-0.187) and diastolic BPV (0.096, 0.007-0.184). High Aß uptake partially mediated the relationship between mean SBP and the Mini-Mental State Examination (MMSE) scores. Hippocampal atrophy mediated the relationship between diastolic BPV and MMSE scores. CONCLUSIONS: Each BP parameter affects Alzheimer's and vascular disease markers differently, which in turn leads to cognitive impairment. Therefore, it is necessary to appropriately control specific BP parameters to prevent the development of dementia. Furthermore, a better understanding of pathways from specific BP parameters to cognitive impairments might enable us to select the managements targeting the specific BP parameters to prevent dementia effectively.

Association of Glycemic Variability With Imaging Markers of Vascular Burden, ß-Amyloid, Brain Atrophy, and Cognitive Impairment.

BACKGROUND AND OBJECTIVE: We aimed to investigate the association between glycemic variability (GV) and neuroimaging markers of white matter hyperintensities (WMH), beta-amyloid (Aß), brain atrophy, and cognitive impairment. METHODS: This was a retrospective cohort study that included participants without dementia from a memory clinic. They all had Aß PET, brain MRI, and standardized neuropsychological tests and had fasting glucose (FG) levels tested more than twice during the study period. We defined GV as the intraindividual visit-to-visit variability in FG levels. Multivariable linear regression and logistic regression were used to identify whether GV was associated with the presence of severe WMH and Aß uptake with DM, mean FG levels, age, sex, hypertension, and presence of APOE4 allele as covariates. Mediation analyses were used to investigate the mediating effect of WMH and Aß uptake on the relationship between GV and brain atrophy and cognition. RESULTS: Among the 688 participants, the mean age was 72.2 years, and the proportion of female participants was 51.9%. Increase in GV was predictive of the presence of severe WMH (coefficient [95% CI] 1.032 [1.012-1.054]; p = 0.002) and increased Aß uptake (1.005 [1.001-1.008]; p = 0.007). Both WMH and increased Aß uptake partially mediated the relationship between GV and frontal-executive dysfunction (GV → WMH → frontal-executive; direct effect, -0.319 [-0.557 to -0.080]; indirect effect, -0.050 [-0.091 to -0.008]) and memory dysfunction (GV → Aß â†’ memory; direct effect, -0.182 [-0.338 to -0.026]; indirect effect, -0.067 [-0.119 to -0.015]), respectively. In addition, increased Aß uptake completely mediated the relationship between GV and hippocampal volume (indirect effect, -1.091 [-2.078 to -0.103]) and partially mediated the relationship between GV and parietal thickness (direct effect, -0.00101 [-0.00185 to -0.00016]; indirect effect, -0.00016 [-0.00032 to -0.000002]). DISCUSSION: Our findings suggest that increased GV is related to vascular and Alzheimer risk factors and neurodegenerative markers, which in turn leads to subsequent cognitive impairment. Furthermore, GV can be considered a potentially modifiable risk factor for dementia prevention.

Is atrial fibrillation always a culprit of stroke in patients with atrial fibrillation plus stroke?

BACKGROUND: Some ischemic strokes in patients with atrial fibrillation (AF) are caused by noncardioembolic etiologies (AF-unrelated stroke), but not AF itself (AF-related stroke). However, most clinical trials on the risk of stroke in AF have not distinguished between these. We investigated the frequency and features of AF-unrelated versus AF-related strokes in patients with AF plus ischemic stroke. We hypothesized that certain clinical factors, including chronicity of AF, treatment at the time of stroke onset and echocardiographic findings, may help to discriminate between AF-related and AF-unrelated strokes. The mechanisms and antithrombotic medications at the time of stroke recurrence in the two groups were also examined. METHODS: Consecutive patients with ischemic stroke within 7 days of symptom onset and with AF were included. Patients were classified according to the previously published criteria. Clinical factors including CHADS2 and CHA2DS2-VASc scores and transthoracic echocardiographic (TTE) findings were evaluated. RESULTS: Of 522 patients, 424 (81.2%) were grouped as AF-related stroke and the remaining 90 (17.2%) were classified as AF-unrelated stroke. Among the patients with AF-unrelated stroke, 51 (9.8%) were categorized as possible large artery atherosclerosis and 38 (7.3%) as possible small artery occlusion; 1 patient (0.2%) was assigned to miscellaneous cause. The AF-related and AF-unrelated strokes had similar CHADS2 and CHA2DS2-VASc scores. However, compared to AF-unrelated stroke, AF-related stroke was independently associated with female sex (odds ratio, OR, 2.19; 95% confidence interval, CI, 1.18-4.05), sustained AF (OR, 2.09; 95% CI, 1.21-3.59), inadequate anticoagulation at stroke onset (OR, 3.21; 95% CI, 1.33-7.75) and left ventricular dysfunction on TTE (OR, 2.84; 95% CI, 1.40-5.74). We identified 26 patients who experienced 2 strokes during the study period. The initial stroke subtype was a strong predictor of the recurrent stroke mechanism (p < 0.001). Among 17 events of AF-related recurrent stroke in these subpopulation, only 2 strokes (11.8%) occurred in a setting of adequate anticoagulation, whereas 4 out of 9 patients (44.4%) who had AF-unrelated strokes at recurrence were sufficiently anticoagulated at the time of admission (p = 0.138). CONCLUSION: AF is not always a culprit of stroke in patients with AF plus ischemic stroke; approximately one sixth of these cases are unrelated to AF and have distinct characteristics compared to AF-related stroke. There are significant differences in terms of some clinical and TTE parameters between AF-related and AF-unrelated stroke. Future studies are warranted to optimize strategies for risk stratification, treatment and prevention of stroke in these patients.